Text Classification
setfit
Safetensors
sentence-transformers
English
bert
sentiment-analysis
few-shot-learning
Eval Results (legacy)
text-embeddings-inference
Instructions to use loganh274/nlp-testing-setfit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use loganh274/nlp-testing-setfit with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("loganh274/nlp-testing-setfit") - sentence-transformers
How to use loganh274/nlp-testing-setfit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("loganh274/nlp-testing-setfit") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
| { | |
| "0": { | |
| "precision": 0.9, | |
| "recall": 0.9, | |
| "f1-score": 0.9, | |
| "support": 20.0 | |
| }, | |
| "1": { | |
| "precision": 0.75, | |
| "recall": 0.75, | |
| "f1-score": 0.75, | |
| "support": 20.0 | |
| }, | |
| "2": { | |
| "precision": 0.7916666666666666, | |
| "recall": 0.95, | |
| "f1-score": 0.8636363636363636, | |
| "support": 20.0 | |
| }, | |
| "3": { | |
| "precision": 1.0, | |
| "recall": 0.8, | |
| "f1-score": 0.8888888888888888, | |
| "support": 20.0 | |
| }, | |
| "4": { | |
| "precision": 1.0, | |
| "recall": 1.0, | |
| "f1-score": 1.0, | |
| "support": 20.0 | |
| }, | |
| "accuracy": 0.88, | |
| "macro avg": { | |
| "precision": 0.8883333333333333, | |
| "recall": 0.8799999999999999, | |
| "f1-score": 0.8805050505050506, | |
| "support": 100.0 | |
| }, | |
| "weighted avg": { | |
| "precision": 0.8883333333333333, | |
| "recall": 0.88, | |
| "f1-score": 0.8805050505050506, | |
| "support": 100.0 | |
| } | |
| } |